| Bootstrap your own latent-a new approach to self-supervised learning JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ... Advances in neural information processing systems 33, 21271-21284, 2020 | 3655 | 2020 |
| Rainbow: Combining improvements in deep reinforcement learning M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ... Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 2029 | 2018 |
| Noisy Networks for Exploration M Fortunato, MG Azar, B Piot International Conference on Learning representations, 0 | 860* | |
| Minimax regret bounds for reinforcement learning MG Azar, I Osband, R Munos International Conference on Machine Learning, 263-272, 2017 | 623 | 2017 |
| Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model M Gheshlaghi Azar, R Munos, HJ Kappen Machine learning 91, 325-349, 2013 | 232 | 2013 |
| Speedy Q-Learning MG Azar, M Ghavamzadeh, HJ Kappen, R Munos Advances in Neural Information Processing Systems, 2411-2419, 2011 | 173* | 2011 |
| Large-scale representation learning on graphs via bootstrapping S Thakoor, C Tallec, MG Azar, M Azabou, EL Dyer, R Munos, P Veličković, ... arXiv preprint arXiv:2102.06514, 2021 | 162* | 2021 |
| The reactor: A fast and sample-efficient actor-critic agent for reinforcement learning A Gruslys, W Dabney, MG Azar, B Piot, M Bellemare, R Munos arXiv preprint arXiv:1704.04651, 2017 | 149* | 2017 |
| Dynamic Policy Programming M Gheshlaghi Azar, V Gomez, HJ Kappen Journal of Machine Learning Research 13, 3207-3245, 2012 | 138 | 2012 |
| Bootstrap latent-predictive representations for multitask reinforcement learning ZD Guo, BA Pires, B Piot, JB Grill, F Altché, R Munos, MG Azar International Conference on Machine Learning, 3875-3886, 2020 | 106 | 2020 |
| Sequential transfer in multi-armed bandit with finite set of models MG Azar, A Lazaric, E Brunskill Advances in Neural Information Processing Systems, 2220-2228, 2013 | 98 | 2013 |
| On the sample complexity of reinforcement learning with a generative model MG Azar, R Munos, B Kappen arXiv preprint arXiv:1206.6461, 2012 | 96 | 2012 |
| Observe and look further: Achieving consistent performance on atari T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ... arXiv preprint arXiv:1805.11593, 2018 | 87 | 2018 |
| Neural predictive belief representations ZD Guo, MG Azar, B Piot, BA Pires, R Munos arXiv preprint arXiv:1811.06407, 2018 | 65 | 2018 |
| Hindsight credit assignment A Harutyunyan, W Dabney, T Mesnard, M Gheshlaghi Azar, B Piot, ... Advances in neural information processing systems 32, 2019 | 63 | 2019 |
| Stochastic optimization of a locally smooth function under correlated bandit feedback MG Azar, A Lazaric, E Brunskill 31st International Conference on Machine Learning (ICML), 2014 | 63* | 2014 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 62 | 2019 |
| Dynamic policy programming with function approximation MG Azar, V Gómez, B Kappen Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 51 | 2011 |
| A cryptography-based approach for movement decoding EL Dyer, M Gheshlaghi Azar, MG Perich, HL Fernandes, S Naufel, ... Nature biomedical engineering 1 (12), 967-976, 2017 | 46 | 2017 |
| Regret bounds for reinforcement learning with policy advice MG Azar, A Lazaric, E Brunskill Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013 | 36 | 2013 |
Rémi MunosDeepMindVerified email at inria.fr
